STUDY GUIDE DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE PDF, VALID DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE TEST DUMPS

Study Guide Databricks-Certified-Data-Engineer-Associate Pdf, Valid Databricks-Certified-Data-Engineer-Associate Test Dumps

Study Guide Databricks-Certified-Data-Engineer-Associate Pdf, Valid Databricks-Certified-Data-Engineer-Associate Test Dumps

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q48-Q53):

NEW QUESTION # 48
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

  • A. Parquet files can be partitioned
  • B. Parquet files have the ability to be optimized
  • C. Parquet files have a well-defined schema
  • D. CREATE TABLE AS SELECT statements cannot be used on files
  • E. Parquet files will become Delta tables

Answer: C

Explanation:
Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.
References: CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV


NEW QUESTION # 49
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

  • A. processingTime(1)
  • B. trigger(continuous="once")
  • C. trigger(parallelBatch=True)
  • D. trigger(availableNow=True)
  • E. trigger(processingTime="once")

Answer: D

Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter


NEW QUESTION # 50
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?

  • A. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
  • B. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
  • C. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
  • D. Records that violate the expectation cause the job to fail.
  • E. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.

Answer: D

Explanation:
Explanation
https://docs.databricks.com/en/delta-live-tables/expectations.html
Action
Result
warn (default)
Invalid records are written to the target; failure is reported as a metric for the dataset.
drop
Invalid records are dropped before data is written to the target; failure is reported as a metrics for the dataset.
fail
Invalid records prevent the update from succeeding. Manual intervention is required before re-processing.


NEW QUESTION # 51
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

  • A. A data lakehouse enables machine learning and artificial Intelligence workloads.
  • B. A data lakehouse supports ACID-compliant transactions.
  • C. A data lakehouse provides storage solutions for structured and unstructured data.
  • D. A data lakehouse stores data in open formats.
  • E. A data lakehouse allows the use of SQL queries to examine data.

Answer: B

Explanation:
Explanation
One of the key features of a data lakehouse that results in improved data quality over a traditional data lake is its support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. ACID transactions provide data integrity and consistency guarantees, ensuring that operations on the data are reliable and that data is not left in an inconsistent state due to failures or concurrent access. In a traditional data lake, such transactional guarantees are often lacking, making it challenging to maintain data quality, especially in scenarios involving multiple data writes, updates, or complex transformations. A data lakehouse, by offering ACID compliance, helps maintain data quality by providing strong consistency and reliability, which is crucial for data pipelines and analytics.


NEW QUESTION # 52
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

  • A. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
  • B. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
  • C. They can reduce the cluster size of the SQL endpoint.
  • D. They can turn on the Auto Stop feature for the SQL endpoint.
  • E. They can set up the dashboard's SQL endpoint to be serverless.

Answer: D

Explanation:
The Auto Stop feature allows the SQL endpoint to automatically stop after a specified period of inactivity.
This can help reduce the cost and resource consumption of the SQL endpoint, as it will only run when it is needed to refresh the dashboard or execute queries. The data engineer can configure the Auto Stop setting for the SQL endpoint from the SQL Endpoints UI, by selecting the desired idle time from the Auto Stop dropdown menu. The default idle time is 120 minutes, but it can be set to as low as 15 minutes or as high as
240 minutes. Alternatively, the data engineer can also use the SQL Endpoints REST API to set the Auto Stop setting programmatically. References: SQL Endpoints UI, SQL Endpoints REST API, Refreshing SQL Dashboard


NEW QUESTION # 53
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